The following article is by Gabe Lowy, a former sell-side technology analyst who is founder of TechTonics Advisors, a provider of strategic communication for technology companies. This is the first article of a two-part series.
Both the buy-side and sell-side need new approaches to investment research. Firms must become more software-driven to improve performance, cost profiles and competitiveness in order to meet the demands of a changing marketplace. This article looks at the buy-side; tomorrow I’ll examine the sell-side.
The Role of Software
Software creates a virtuous cycle in the enterprise. In sector after sector, it’s reduced barriers to entry, causing market disruption. Software enables faster innovation that can be brought to market through more efficient delivery systems. This intensifies competition as new entrants challenge established players rushing to adapt. Users gain more choice and voice, which raises the stakes on companies to provide exceptional user experience. And that user experience is delivered by software.
Exhibit 1. How Software Impacts Business
Software is transforming the investment management industry by shifting business models away from high-cost, low-value proprietary products to low-cost, high-quality fiduciary services. Software’s virtuous cycle should catalyze investment firms to redirect IT budgets away from static, database-centric systems of record that focus on historical reporting toward real-time, event-driven dynamic systems of engagement to improve outcomes at the point of decision.
Does More Data Beget Better Research?
The commission business in the US and Europe is a shrinking – albeit still large – market. Greenwich Associates estimates that large U.S. institutional investors pay about $6 billion per year in trading commissions as compensation to brokers and other providers of research and advisory services. There are many explanations for why the commission business continues to shrink. Principal among them is the shift in assets away from actively-managed funds toward indexing and ETFs and ETPs. This asset shift reflects chronic underperformance and high fees across asset classes, exacerbated by style drift and high portfolio turnover.
Despite an explosion in data sources – and the insatiable demand for more data – most investment managers cannot consistently and accurately identify the input variables that drive portfolio performance. Yet as costs for data sources and data management continue to rise, the return on data-driven research inputs continues to decline. How can portfolios be constructed without a fundamental understanding of the input variables that drive their investment themes?
Yet fewer and fewer portfolio managers and analysts are doing long-term fundamental research. They are reluctant to take a longer-term approach because they fear that underperformance for more than a quarter or two will result in client withdrawals.
Moreover, the buy-side remains heavily dependent on the sell-side for sector and company-specific research and corporate access. This is despite the fact that most buy-siders complain about the quality, independence and cost/value of that research. And with new regulations on the horizon, the buy-side is likely to cut back significantly if they have to pay cash for research.
A New Software-Driven Environment
Demographics, regulation and technology are fundamentally changing the investment landscape and the quality of research services investors will receive.
- Gen X, Gen Y and Millennials have given rise to the “low-touch”, mobile, self-service investor;
- New fiduciary “best interest” and MiFID regulations will disrupt research product and delivery processes; and,
- New software development and delivery practices, data integration and management, and advanced analytics put greater emphasis on application performance and user experience.
It’s now common for users – both employees and customers – to interact with enterprises exclusively through apps. And more often than not, we’re accessing these apps with a mobile device. Cloud, mobile and social megatrends have been the key drivers. As a result, the quality of software will only grow in importance in defining and differentiating business models.
We’ve now got access to an ever-increasingly number of apps and data sources that puts information at our fingertips within seconds. Users are empowered with more choice than ever before and social media has amplified their voice. It’s no longer enough for applications to work; they must now perform to user expectations.
This environment warrants new processes and technologies, such as:
- DevOps. Build higher-quality software faster and more efficiently using agile methods that promote more communicative and collaborative IT teams.
- dPaaS. Data platform-as-a-service unifies integration and data management focuses on data quality to improve decision outcomes and GRC.
- Advanced analytics. In-memory database, parallel processing, and data visualization tools to analyze larger data sets faster and build more accurate models.
- PADS Framework. Next-gen application performance monitoring and operational intelligence to optimize user experience and efficiency.
Software-based automated investment advisors are disrupting the mutual fund market. They are leveraging technology to provide low-cost, high-quality services that are highly appealing to customers. Competitors such as Betterment, FutureAdvisor, Rebalance IRA, Wealthfront create portfolios of index funds and ETFs with no trading commissions. They include research tools that enable investors to construct, monitor and manage their portfolios on a daily basis.
The Way Forward for Research
To compete with this threat, investment firms need to become more software-driven. They can differentiate by combining a longer-term investment focus with modern technology tools. To be more competitive, they must tackle their data management issues by leveraging technology to understand the input variables that drive portfolio performance. And firms must invest in software platforms that facilitate their move to cloud, mobile and social.
Firms may consider creating a centralized Research Curator (RC) team to optimize research sources. RC is a software-driven function that performs integration, analytics and data management to improve product quality and performance. The team’s principal purpose is to flesh out the macro and micro key performance variables (KPVs) that drive portfolio performance. The team can then use internal models to streamline processes for selecting and paying external research providers.
The RC team begins by taking inventory of the research sources currently being used by all PMs and analysts. Based on data analyses of the KPVs, or input variables that drive portfolio performance, the RC team is responsible for marshaling resources to those research providers that correlate with those KPVs, while eliminating those that do not. This is a rolling process as market condition change. However, if fund managers stick to their funds’ stated styles, the most critical KPVs may not change significantly over time.
Investment teams will be required to justify why additional research sources should be acquired. With chronic underperformance exposing inefficient or underutilization of research sources, the decision to acquire sources must be firmly based on data, rather than on “best judgment” assumptions, “conventional wisdom” or past relationships. As such, the RC team manages vendor relationships and contracts. Finally, the RC team must also monitor and anticipate regulatory developments. For example, proposals under MiFID II would enable the RC team to rationalize data sources and ensure compliance.
Amid the hype about big data, we suggest firms start with better data. This begins by focusing on data quality to make certain that the right information is available to PMs and analysts in a timely fashion. The more they trust the data they are working with, the more reliable their models and algorithms will be. Firms can then integrate appropriate big data into workflows gradually. We also suggest firms support sector/country experts with quant models developed by the analytics team.
By returning to a longer-term perspective, firms can improve differentiation and competitiveness – while deploying technologies that can offer the same types of services that automated investment advisors provide. A longer-term focus would also allow firms to reduce costs through lower portfolio turnover and better use of technology to rationalize data sources.